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The huge amount of information available online has given rise to personalization and filtering systems. Recommender systems (RS) constitute a specific type of information filtering technique that present items according to user's interests. In this research, a web-based personalized recommender system capable of providing learners with books that suit their reading abilities was developed. Content-based filtering (CBF) was used to analyze learners' reading abilities while books that are found suitable to learners are recommended with fuzzy matching techniques. The yokefellow cold-start problem inherent to CBF is assuaged by cold start engine. An experimental study was carried out on a database of 10000 books from different categories of computing studies. The outcome tracked over a period of eight months shows that the proposed system induces greater user satisfaction and this attests users' desirability of the system.<\/p>","DOI":"10.4018\/ijwltt.2014010102","type":"journal-article","created":{"date-parts":[[2014,6,17]],"date-time":"2014-06-17T12:40:44Z","timestamp":1403008844000},"page":"18-32","source":"Crossref","is-referenced-by-count":10,"title":["Personalized Recommender System for Digital Libraries"],"prefix":"10.4018","volume":"9","author":[{"given":"Omisore","family":"M. O.","sequence":"first","affiliation":[{"name":"Department of Computer Science, Federal University of Technology Akure, Akure, Ondo, Nigeria"}]},{"given":"Samuel","family":"O. 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